GitHub - kassambara/ggcorrplot: Visualization of a correlation matrix using ggplot2 (original) (raw)

ggcorrplot: Visualization of a correlation matrix using ggplot2

The ggcorrplot package can be used to visualize easily acorrelation matrix using ggplot2. It provides a solution forreordering the correlation matrix and displays the significance level on the correlogram. It includes also a function for computing a matrix of correlation p-values.

Find out more athttp://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.

Installation and loading

ggcorrplot can be installed from CRAN as follow:

install.packages("ggcorrplot")

Or, install the latest version from GitHub:

Install

if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/ggcorrplot")

Loading

library(ggcorrplot)

Getting started

Compute a correlation matrix

The mtcars data set will be used in the following R code. The functioncor_pmat() [in ggcorrplot] computes a matrix of correlation p-values.

Compute a correlation matrix

data(mtcars) corr <- round(cor(mtcars), 1) head(corr[, 1:6]) #> mpg cyl disp hp drat wt #> mpg 1.0 -0.9 -0.8 -0.8 0.7 -0.9 #> cyl -0.9 1.0 0.9 0.8 -0.7 0.8 #> disp -0.8 0.9 1.0 0.8 -0.7 0.9 #> hp -0.8 0.8 0.8 1.0 -0.4 0.7 #> drat 0.7 -0.7 -0.7 -0.4 1.0 -0.7 #> wt -0.9 0.8 0.9 0.7 -0.7 1.0

Compute a matrix of correlation p-values

p.mat <- cor_pmat(mtcars) head(p.mat[, 1:4]) #> mpg cyl disp hp #> mpg 0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07 #> cyl 6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09 #> disp 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08 #> hp 1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00 #> drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 #> wt 1.293959e-10 1.217567e-07 1.222320e-11 4.145827e-05

Correlation matrix visualization

Visualize the correlation matrix

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method = "square" (default)

ggcorrplot(corr)

method = "circle"

ggcorrplot(corr, method = "circle") #> Warning: guides(<scale> = FALSE) is deprecated. Please use guides(<scale> = #> "none") instead.

Reordering the correlation matrix

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using hierarchical clustering

ggcorrplot(corr, hc.order = TRUE, outline.color = "white")

Types of correlogram layout

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Get the lower triangle

ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white")

Get the upper triangle

ggcorrplot(corr, hc.order = TRUE, type = "upper", outline.color = "white")

Change colors and theme

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Argument colors

ggcorrplot( corr, hc.order = TRUE, type = "lower", outline.color = "white", ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726") )

Add correlation coefficients

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argument lab = TRUE

ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = TRUE)

Add correlation significance level

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Argument p.mat

Barring the no significant coefficient

ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat)

Leave blank on no significant coefficient

ggcorrplot( corr, p.mat = p.mat, hc.order = TRUE, type = "lower", insig = "blank" )